Modelling Creativity: Identifying Key Components through a Corpus-Based Approach

Jordanous, Anna, Keller, Bill (2016) Modelling Creativity: Identifying Key Components through a Corpus-Based Approach. PLoS ONE, 11 (10). pp. 1-27. ISSN 1932-6203. (doi:10.1371/journal.pone.0162959)

Abstract

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Item Type: Article
DOI/Identification number: 10.1371/journal.pone.0162959
Uncontrolled keywords: computational creativity; model of creativity; statistical natural language processing; corpora; information retrieval
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Anna Jordanous
Date Deposited: 29 Sep 2016 10:46 UTC
Last Modified: 29 May 2019 17:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57231 (The current URI for this page, for reference purposes)
Jordanous, Anna: https://orcid.org/0000-0003-2076-8642
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